• 소성∙가공
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• 제4권3호
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• pp.214-221
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• 1995

In the paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in the neural network. The architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of a neural network, an optimal billet is determined by applying the nonlinear mathematical relationship between the aspect ratios in the initial billet and the final products. The amount of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet aspect ratios and those of the unfilled volumes. After learning, the system is able to predict the filling regions which are exactly the same or slightly different to the results of finite element simulation. This new method is applied to find the optimal billet size for the plane strain rib-web product in cold forging. This would reduce the number of finite element simulation for determining the optimal billet size of forging product, further it is usefully adapted to physical modeling for the forging design.

• 한국정밀공학회지
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• 제14권2호
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• pp.129-135
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• 1997

Experimental studies have been carried out to obtain uniform cups by one operation of backward extrusion. A lot of factors on dimensional accuracy of backward extruded cups are billet material, billet shape, punch shape, punch velocity, geometry of tool, tool material, and lubrication etc. In manufacturing cup-shaped parts by backward extrusion, it is very important to design the initial billet shape or the preform. The objective of this paper is to find that the shape of the initial billet is related to dimensional accuracy and also to manufacture the more accurate product simultaneously reducing the loss of material as forming the shape of the initial billet by means of upsetting.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.217-222
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• 1994

This paper describes the determination of optimal initial billet size for axisymmetric cold forging products using neural networks. The determination of optimal initial billet size is very important in forging design and forming sequence design, because the result of such designs and forming load can be different by variable initial billet sizes. The forming difficulty has been defined as the degree of difficulty in forming by 3 process ' forward extrusion, backward extrusion and upsetting. By neural networks a forming difficulty can be determined with the ratio of shape and forming process. From the graph of maximum, minimum, and average forming difficulties by variable billet sizes, the optimal billet size can be determined. The initial billets of a solid part and a hollow part whichwas determined by this study are compared with the sequence drawing generated by the one of forming sequence design system.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.196-200
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• 1996

In general, cylinderical types of billet are use in the backward extrusion. It is difficult to obtain homogenious wall thickness by the backward extrusion using these. It is gradually increased that improving the accuracyand reducing the post machining of the final products. In manufacturing cup shaped parts by backward extrusion, it is very important to design optimal initial billet or preform. These can improve the accuracy of final products and remove the post machining processes. In this study, the influence of final parts geometry by the shape of initial billet as non machined types are discussed.

• 한국정밀공학회지
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• 제12권11호
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• pp.118-124
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• 1995

In the paper, we have proposed a new technique to determine the initial billet for the forged products using a function approximation in neural network. A three-layer neural network is used and a back propagation algorithm is employed to train the network. An optimal billet which satisfied the forming limitation, minimum of incomplete filling in the die cavity, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of function approximation of the neural network. The amount of incomplete filling in the die, load and forming energy as well as effective strain are measured by the rigid-plastic finite element method. This new technique is applied to find the optimal billet size for the axisymmetric rib-web product in hot forging. This would reduce the number of finite element simulation for determining the optimal billet of forging products, further it is usefully adopted to physical modeling for the forging design

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1994년도 추계학술대회 논문집
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• pp.133-140
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• 1994

In this paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in neural networks. the architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of neural network, an optimal billet is determined by applying nonlinear mathematical relationship between shape ratio in the initial billet and the final products. A volume of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet shape ratio and that of the un-filled volume. After learning, the system is able to predict the filling region which are exactly the same or slightly different to results of finite element method. It is found that the prediction of the filling shape ratio region can be made successfully and the finite element method results are represented better by the neural network.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.198-203
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• 1995

In the paper, we have proposed a new technique to detemine the initial billet for the forged products using a function approximation in neural network. A three-layer neural network is used and a back propagation algorithm is employed totrain the network. An optimal billet which satisfied the forming limitation, minimum of incomplete filling in the die cavity, load and energyas well as more uniform distribution of effective strain, is determined by applying the ability of function approximation of te neural network. The amount of incomplete filling in the die, load and forming energyas well as effective strain are measured by the rigid-plastic finite element method. The new technique is applied tofind the optimal billet size for the axisymmetric rib-web product in hot forging. This would reduce the number of finite element simulation for determing the optimal billet of forging products, further it is usefully adapted to physical modeling for the forging design.

• 대한기계학회논문집
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• 제16권8호
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• pp.1468-1484
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• 1992

본 연구에서는 유한요소법을 이용해서 초기형상을 결정하는 새로운 방법으로 서 전방 투사법을 제안하고자 한다. 전방 투사법으로서 선형 보간을 이용한 방법과 소성 문제의 물리적인 특성을 고려하여 퍼지 로직을 도입한 퍼지시스템을 개발하려 한 다. 선형보간을 이용한 전방투사법은 임의의 초기 형상에 대한 유한 요소 해석 결과 얻어진 최종 형상에서의 미 충만 부피를 선형 보간하여 초기 형상에 적용함으로서 최 적 초기 형상을 결정하는 방법이다. 그러나 미 충만 부피의 변화가 미소할때에는 쉽 게 최적 초기 값을 찾지 못하는 경우가 발생하므로 유동 특성을 고려한 퍼지 로직을 구성하여 퍼지 시스템을 개발하였다. 이 방법을 리브-웨브(rbi-web)형태의 축대칭 단조 문제에 적용하고 유한 요소법에 의한 해석중 격자 재구성의 필요에 의해 단위체 격자 재구성법을 이용한다. 결정해야될 초기 형상의 변수로서는 형상비(aspect ra- tio=높이/지름)을 고려하기로 한다.

• 한국정밀공학회지
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• 제11권5호
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• pp.134-142
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• 1994

An upper bound elemental technique (UBET) is applied to predict variations of neutral plane and optimal position of the initial billet for rib-wep shaped ring forging. In the analysis, the neutral plane position and velocity fields are determined by minimizing the total power consump- tion with respect to chosen parameters. The degree of die-cavity filling by initial billet-position and the variations of neutral plane by friction condition are investigated. Experiments have been carried out with pure plasticine billets at room temperature. The theoretical predictions of the forging load and the flow pattern are in good agrement with the experimental results.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 춘계학술대회 논문집
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• pp.91-94
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• 2004

Preform design techniques have been investigated in efforts to reduce die wear and forming load and to improve material flow, filing ratio, etc. In hot forging processes, a thin deformed part of a workpiece, known as a flash, is formed in the narrow gap between the upper and lower tools. Although designers make tools that generate a flash intentionally in order to improve flow properties, excessive flash increases die wear and forming load. Therefore, it is necessary to make a preform shape that can reduce the excessive flash without changing flow properties. In this paper, a new preform design technique is proposed to reduce the excessive flash in a metal forging process. After a finite element simulation of the process is carried out with an initial billet, the flow of material in the flash region is traced from the final shape to the initial billet. The region belonging to the flash is then easily found in the initial billet. The finite element simulation is then carried out again with the modified billet from which the selected region has been removed. In several iterations of this technique, the optimal preform shape that minimizes the amount of flash without changing the forgeability can be obtained.